The present invention generally relates to a testing, classification, analysis system (TCAS) including an artificial learning counter surveillance system (ALCSS) used in relation to various optical systems to determine degradation or disruption or a self-protection surveillance system (SPSS) for protecting electro-optic assets.
A need was identified for a capability to provide protection against and ability to create degradation and disruption against electro-optic systems of interest using non-destructive directed energy systems capable of avoiding permanent destruction of a system of interest. For example, laser countermeasures frequently cause permanent damage to a system they are directing energy against such as a surveillance or UAV system with a camera or optics system.
Embodiments of the invention can enable use of a directed energy system, e.g., laser energy, to either ensure or prevent a platform or entity with one or more electro-optic systems to perform its designated or predetermined mission without damage to the platform and the electro-optic system. Embodiments of the invention are capable of either causing or protecting against a range of degradations or disruptions including minor degradation of the entity or platform's electro-optic system to complete disruption. Embodiments include methods and systems that are adapted to measure effectiveness of a directed energy system in disrupting or degrading an electro-optics system of interest.
Existing systems were inadequate as they did not have necessary information or control systems and control logic. They were highly dependent on subjective human analysis which was time consuming, not real time, and highly unreliable. Existing systems included laser countermeasures analysis systems that used very different analysis approaches. For example, analysis relied solely or largely on the human vision system (HVS), which is subjective. Additionally, existing countermeasures systems have a capability limited to a binary relationship with an adversary's targets, either not engaged or engaged to destroy. The exemplary HVS was used to determine effectiveness of an engagement (destroyed/not destroyed).
An advantage of the embodiments or an example of the invention includes a capability to create a system or countermeasures that are not limited to such a binary relationship. Additionally, embodiments of the invention can enable analysis of effectiveness of a counter measure with respect to an electro-optic system of interest employing systematic, machine learning algorithms that allow users to select specific video metrics to be used in classifying severity of degradation or disruption associated with the optical system of interest. Now a system can enable operations supporting (generating or protecting against) varying levels of disruption or degradation engagement types (e.g., minor degradation to complete disruption) on the adversary's electro-optic systems and have accompanying quantifiable results for analysis.
Embodiments of the invention can include analysis systems that automate video analysis for directed energy and electro-optic countermeasure uses by determining the effectiveness of each laser shot. An exemplary machine learning algorithm establishes a systematic approach to analyzing data that originally seemed to diverge from an obvious pattern for given video metrics. Furthermore, video processing approaches without machine learning requires constant human interaction, which drastically increases the time commitment to parse through data. Exemplary implementation of machine learning algorithms in embodiments of the invention not only allows for a systematic analysis but significantly increases efficiency. Embodiments of the invention also enable quantification of effectiveness of directed energy to disrupt or degrade an electro-optic system of interest whereas the HVS was only able to qualify such effectiveness. This new capability enables an entirely new way to report or determine effectiveness in the surveillance protection or privacy contexts.
Additionally, an embodiment includes video metrics (structural similarity metrics, power spectral density, and radius of saturated pixels) that were selected by using a video or image feature selection program, e.g., within MATLAB®, then further validated through hands-on testing. The feature selection program in MATLAB used advanced statistics to compare the structural similarity metric, power spectral density, radius of saturated pixels number of saturated pixels, and pixel histogram to verify which metrics were most useful. To double-check the selection of the metrics after the feature selection program, the metrics were validated through hands on testing which included running dozens of datasets through multiple combinations of the metrics.
Additional features and advantages of the present invention will become apparent to those skilled in the art upon consideration of the following detailed description of the illustrative embodiment exemplifying the best mode of carrying out the invention as presently perceived.